20230127840. AUTONOMOUS WORKLOAD HOMING IN A MULTI-TENANT ENVIRONMENT simplified abstract (Dell Products L.P.)

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AUTONOMOUS WORKLOAD HOMING IN A MULTI-TENANT ENVIRONMENT

Organization Name

Dell Products L.P.

Inventor(s)

Ashish A. Pancholi of Cary NC (US)

Bina K. Thakkar of Cary NC (US)

David C. Waser of Holly Springs NC (US)

AUTONOMOUS WORKLOAD HOMING IN A MULTI-TENANT ENVIRONMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230127840 titled 'AUTONOMOUS WORKLOAD HOMING IN A MULTI-TENANT ENVIRONMENT

Simplified Explanation

The abstract describes a method for identifying a suitable storage array for an application workload in a multi-tenant environment.

  • The method involves identifying workload parameters and grouping them into bins based on historical data.
  • The bin mix, which indicates workload activity exceeding a specified threshold, is used to train a supervised learning model.
  • The model is trained using a generative adversarial network to infer attributes of a suitable storage array.
  • The workload may be associated with a scaling factor that influences the determination of a suitable storage array.

Potential Applications

  • This technology can be applied in multi-tenant cloud environments to optimize storage allocation for different application workloads.
  • It can be used by cloud service providers to improve resource utilization and performance for their customers.

Problems Solved

  • The method solves the problem of efficiently allocating storage resources in a multi-tenant environment.
  • It addresses the challenge of determining the most suitable storage array for different application workloads.

Benefits

  • The method allows for better resource utilization by matching application workloads with the most appropriate storage arrays.
  • It improves performance and efficiency by dynamically allocating storage resources based on workload characteristics.
  • The use of a generative adversarial network enhances the accuracy and effectiveness of the model in predicting suitable storage arrays.


Original Abstract Submitted

a methods for identifying a multi-tenant storage array for an application workload includes identifying workload parameters and defining a plurality of groups for each parameter and a plurality of “bins” corresponding to tuples of the groups. exemplary workload parameters include a percent read parameter and an i/o size parameter. a bin mix of the workload is determined based on historical data wherein the bin mix indicates bins associated with workload activity exceeding a specified threshold. the bin mix is used to define at least some inputs for a supervised learning model of a process for homing application workloads in a multi-tenant storage array. after appropriate training of the model with a generative adversarial network, the model may be invoked to infer or predict attributes of a suitable storage array. the workload may be associated with a scaling factor that influences the determination of a suitable storage array.

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